Background The contamination of public baths with Legionella pneumophila contamination has become a growing public health concern in recent years. However, research on its association with bacterial community characteristics in water samples remains limited. The integration of 16S rRNA sequencing and random forest modeling provides a new approach to elucidate the bacterial community characteristics of public bath water and their association with Legionella pneumophila contamination. Objective To investigate the bacterial community structure and diversity of public bath water in Shanghai, explore the association between Legionella pneumophila contamination and bacterial community characteristics, and identify key bacterial genera associated with contamination, thereby providing a scientific basis for formulating hygiene management regulations for public bath water. Methods From February to March 2023, water samples were collected from ten public baths in Shanghai which were selected based on business scale, regional distribution, and functional differences. Water quality parameters were evaluated, and the samples were categorized into Legionella-positive and Legionella-negative groups based on the detection results of Legionella pneumophila. The bacterial community structure, α-diversity, and β-diversity were analyzed using 16S rRNA sequencing. Redundancy analysis (RDA) was employed to examine the relationship between physicochemical factors and bacterial community diversity. A random forest model was employed to identify key bacterial genera distinguishing the two groups, with the importance of genera being evaluated based on the mean decrease accuracy (MDA). Results The oxygen consumption in the Legionella-positive group was significantly lower than that in the Legionella-negative group (mean values: 1.85 mg·L⁻¹ vs. 6.81 mg·L⁻¹, P< 0.05), while no significant differences were observed in other physicochemical indicators. The sequencing results revealed a total of 27 bacterial phyla and 454 bacterial genera, with Proteobacteria (63.00%) being the dominant phylum. The dominant genera included Pelomonas (8.50%), Acidovorax (8.13%), Mycobacterium (7.93%), and Acinetobacter (6.59%). The α-diversity analysis indicated that bacterial community richness (Chao1 and ACE indices) was significantly higher in the Legionella-positive group than in the Legionella-negative group (P<0.01). The β-diversity analysis showed no significant difference in the bacterial community structure between the two groups (P>0.05). The RDA analysis demonstrated that the bacterial community diversity was positively correlated with pH and negatively correlated with oxygen consumption and free residual chlorine. The RDA1 and RDA2 explained 23.92% and 21.30% of the bacterial community diversity, respectively. The random forest model identified 20 key genera significantly influencing the microbial community distribution between the two groups, including unclassified_Bradyrhizobiaceae (MDA=2.42), Meiothermus (MDA=2.37), and Flavihumibacter (MDA=2.26). Conclusion The diversity of bacterial communities in public bath water is influenced by pH, oxygen consumption, and free residual chlorine. Samples contaminated with Legionella pneumophila exhibit greater microbial richness and contain characteristic key bacterial genera that contribute to community differences. Machine learning random forest technology helps identify these distinctive key bacterial genera. The findings provide a basis for carrying out risk early warning strategies in such settings.

Objective To assess the evolving disease burden of esophageal and gastric cancers in China from 1990 to 2021, with a focus on gender disparities, and construct a predictive model to forecast disease trends from 2022 to 2031, aiming to optimize targeted prevention strategies. Methods Epidemiological data for esophageal and gastric cancers in China (1990-2021) were extracted from the Global Burden of Disease (GBD) 2021 database. Temporal trends were analyzed using Joinpoint regression (version 4.9.1.0), and future trends were predicted via the GM (1, 1) model under grey system theory. Results From 1990 to 2021, tobacco- and alcohol-attributable burdens of esophageal cancer increased, while tobacco- and diet-related burdens of gastric cancer showed no significant change. Deaths and disability-adjusted life years (DALY) for esophageal cancer rose by 40.61% and 17.89%, respectively; gastric cancer deaths increased by 18.95%, though DALY decreased by 1.22%. Both cancers exhibited significant declines in age-standardized mortality rates (−45.78% for esophageal cancer, −53.29% for gastric cancer) and age-standardized DALY rates (−51.45% for esophageal cancer, −57.58% for gastric cancer). China’s age-standardized mortality and DALY rates for both cancers remained consistently higher than global averages. Males exhibited disproportionately higher burdens than females. Predictive modeling projected continued but decelerating declines in disease burdens for both cancers by 2031. Conclusion Over three decades, China achieves measurable reductions in esophageal and gastric cancer burdens, though gastric cancer burdens remain higher than esophageal cancer. Persistent disparities relative to global levels, elevated male burdens, and aging demographics highlight the urgency for prioritized interventions targeting high-risk populations.

Neuropathic pain is frequently comorbidity with cognitive deficits. Neuralized1 (Neurl1)-mediated ubiquitination of CPEB3 in the hippocampus is critical in learning and memory. However, the role of Neurl1 in the cognitive impairment in neuropathic pain remains elusive. Herein, we found that lumbar 5 spinal nerve ligation (SNL) in male rat-induced neuropathic pain was followed by learning and memory deficits and LTP impairment in the hippocampus. The Neurl1 expression in the hippocampal CA1 was decreased after SNL. And this decrease paralleled the reduction of ubiquitinated-CPEB3 level and reduced production of GluA1 and GluA2. Overexpression of Neurl1 in the CA1 rescued cognitive deficits and LTP impairment, and reversed the reduction of ubiquitinated-CPEB3 level and the decrease of GluA1 and GluA2 production following SNL. Specific knockdown of Neurl1 or CPEB3 in bilateral hippocampal CA1 in naïve rats resulted in cognitive deficits and impairment of synaptic plasticity. The rescued cognitive function and synaptic plasticity by the treatment of overexpression of Neurl1 before SNL were counteracted by the knockdown of CPEB3 in the CA1. Collectively, the above results suggest that the downregulation of Neurl1 through reducing CPEB3 ubiquitination and, in turn, repressing GluA1 and GluA2 production and mediating synaptic plasticity impairment in hippocampal CA1 leads to the genesis of cognitive deficits in neuropathic pain.
Animals ; Male ; Neuralgia/metabolism* ; Rats ; Down-Regulation/physiology* ; Ubiquitination/physiology* ; Neuronal Plasticity/physiology* ; Rats, Sprague-Dawley ; CA1 Region, Hippocampal/metabolism* ; Cognitive Dysfunction/metabolism* ; RNA-Binding Proteins/metabolism* ; Receptors, AMPA/metabolism*

This study aimed to comprehensively examine the association of gallstones, cholecystectomy, and cancer risk. Multivariable logistic regressions were performed to estimate the observational associations of gallstones and cholecystectomy with cancer risk, using data from a nationwide cohort involving 239 799 participants. General and gender-specific two-sample Mendelian randomization (MR) analysis was further conducted to assess the causalities of the observed associations. Observationally, a history of gallstones without cholecystectomy was associated with a high risk of stomach cancer (adjusted odds ratio (aOR)=2.54, 95% confidence interval (CI) 1.50-4.28), liver and bile duct cancer (aOR=2.46, 95% CI 1.17-5.16), kidney cancer (aOR=2.04, 95% CI 1.05-3.94), and bladder cancer (aOR=2.23, 95% CI 1.01-5.13) in the general population, as well as cervical cancer (aOR=1.69, 95% CI 1.12-2.56) in women. Moreover, cholecystectomy was associated with high odds of stomach cancer (aOR=2.41, 95% CI 1.29-4.49), colorectal cancer (aOR=1.83, 95% CI 1.18-2.85), and cancer of liver and bile duct (aOR=2.58, 95% CI 1.11-6.02). MR analysis only supported the causal effect of gallstones on stomach, liver and bile duct, kidney, and bladder cancer. This study added evidence to the causal effect of gallstones on stomach, liver and bile duct, kidney, and bladder cancer, highlighting the importance of cancer screening in individuals with gallstones.
Humans ; Mendelian Randomization Analysis ; Gallstones/complications* ; Female ; Male ; Cholecystectomy/statistics & numerical data* ; Middle Aged ; Risk Factors ; Aged ; Adult ; Neoplasms/etiology* ; Stomach Neoplasms/epidemiology*

Liver fibrosis is a vital cause of morbidity in patients with liver diseases and developing novel anti-fibrotic drugs is imperative. Isovalerylspiramycin I (ISP I) as a major component of carrimycin applied to upper respiratory infections, was first found to possess anti-fibrotic potential. The present study aims to evaluate the functions and mechanisms of ISP I in protecting against liver fibrosis. According to our results, ISP I not only reduced the expressions of fibrogenic markers in LX-2 cells but also appeared great protective effects on liver injury and liver fibrosis in bile duct ligation (BDL) rats and carbon tetrachloride (CCl₄) mice. We proved that nucleotide-binding protein 2 (NUBP2) was the direct target of ISP I. ISP I through targeting NUBP2, increased the amount of vascular non-inflammatory molecule-1 (VNN1) on the cell membrane, which will inhibit oxidative stress and fibrosis. Simultaneously, the original carrimycin's protective effect on liver damage and fibrosis was verified. Therefore, our study provides potential agents for patients with liver fibrosis-related diseases, and the clear mechanism supports wide application in the clinic.

[This corrects the article DOI: 10.1016/j.jpha.2023.08.006.].

To effectively exploit the tumor microenvironment (TME), TME-responsive nanocarriers based on cascade reactions have received much attention. In this study, we designed a novel nanoparticle PB@SiO₂@MnO₂@P-Arg (PMP) to construct a cascade reaction nanoplatform. While using biosafety Prussian blue (PB) for photothermal therapy (PTT), this nanoplatform uses silica (SiO₂) as an intermediate layer to assemble Prussian blue and manganese dioxide (MnO₂) into a core-shell structure, which effectively enhances the response of the nanoplatform to TME and promotes the effect of chemodynamic therapy (CDT) resulting from glutathione (GSH) depletion and Fenton-like reaction. The released Mn²⁺ can also be used for magnetic resonance imaging (MRI). Through the cascade reaction, poly-l-arginine (P-Arg) coated on the surface of the nanoparticles can react with hydroxyl radical (•OH) obtained from the Fenton-like reaction to release nitric oxide (NO), which further reacts with O₂•^- to produce the more toxic peroxynitrite anion (ONOO^-). The photothermal effect of PB further enhances the effect of the cascade reaction while reducing the amount of heat required for treatment. In vitro and in vivo studies confirmed the antitumor effects of cascade reaction-based nanoplatforms in combined photothermal/chemodynamic/gas cancer therapies, providing new strategies for the design and fabrication of multifunctional nanoplatforms that integrate diagnostic and therapeutic functions, as well as the application of cascade reactions in multimodal synergistic therapy.

OBJECTIVE: To construct machine learning prediction model for sepsis-associated encephalopathy (SAE), and analyze the application value of the model on early identification of SAE risk in elderly septic patients. METHODS: Patients aged over 60 years with a primary diagnosis of sepsis admitted to intensive care unit (ICU) from 2008 to 2023 were selected from Medical Information Mart for Intensive Care-IV 2.2 (MIMIC-IV 2.2). Demographic variables, disease severity scores, comorbidities, interventions, laboratory indicators, and hospitalization details were collected. Key factors associated with SAE were identified using univariate Logistic regression analysis. The data were randomly divided into training and validation sets in a 7 : 3 ratio. Multivariable Logistic regression analysis was conducted in the training set and visualized using a nomogram model for prediction of SAE. The discrimination of the model was evaluated in the validation set using the receiver operator characteristic curve (ROC curve), and its calibration was assessed using calibration curve. Furthermore, multiple machine learning algorithms, including multi-layer perceptron (MLP), support vector machine (SVM), naive bayes (NB), gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGB), were constructed in the training set. Their predictive performance was subsequently evaluated on the validation set. Taking the XGB model as an example, the interpretability of the model through the SHapley Additive exPlanations (SHAP) algorithm was enhanced to identify the key predictive factors and their contributions. RESULTS: A total of 2 204 septic patients were finally enrolled, of whom 840 developed SAE (38.1%). A total of 21 variables associated with SAE were screened through univariate Logistic regression analysis. Multivariable Logistic regression analysis showed that endotracheal intubation [odds ratio (OR) = 0.40, 95% confidence interval (95%CI) was 0.19-0.88, P < 0.001], oxygen therapy (OR = 0.76, 95%CI was 0.53-0.95, P = 0.023), tracheotomy (OR = 0.20, 95%CI was 0.07-0.53, P < 0.001), continuous renal replacement therapy (CRRT; OR = 0.32, 95%CI was 0.15-0.70, P < 0.001), cerebrovascular disease (OR = 0.31, 95%CI was 0.16-0.60, P < 0.001), rheumatic disease (OR = 0.44, 95%CI was 0.19-0.99, P < 0.001), male (OR = 0.68, 95%CI was 0.54-0.86, P = 0.001), and maximum anion gap (AG; OR = 0.95, 95%CI was 0.93-0.97, P < 0.001) were associated with an decreased probability of SAE, and age (OR = 1.05, 95%CI was 1.03-1.06, P < 0.001), acute physiology score III (APSIII; OR = 1.02, 95%CI was 1.01-1.02, P < 0.001), Oxford acute severity of illness score (OASIS; OR = 1.04, 95%CI was 1.03-1.06, P < 0.001), and length of hospital stay (OR = 1.01, 95%CI was 1.01-1.02, P < 0.001) were associated with an increased probability of SAE. A nomogram model was constructed based on these variables. In the validation set, ROC curve analysis showed that the model achieved an area under the ROC curve (AUC) of 0.723, and the calibration curve showed good consistency between the predicted probability of the model and the observed probability. Among the machine learning algorithms, including MLP, SVM, NB, GBM, RF, and XGB, the SVM model and RF model demonstrated relatively good predictive performance, with AUC of 0.748 and 0.739, respectively, and the sensitivity was both exceeding 85%. The predictive performance of the XGB model was explained through SHAP analysis, and the results indicated that APSIII score (SHAP value was 0.871), age (SHAP value was 0.521), and OASIS score (SHAP value was 0.443) were important factors affecting the predictive performance of the model. CONCLUSIONS The machine learning-based SAE prediction model exhibits good predictive capability and holds significant application value for the early identification of SAE risk in elderly septic patients.
Humans ; Machine Learning ; Aged ; Sepsis-Associated Encephalopathy ; Sepsis/complications* ; Intensive Care Units ; Logistic Models ; Middle Aged ; Male ; ROC Curve ; Female ; Bayes Theorem ; Nomograms ; Support Vector Machine ; Algorithms

BACKGROUND: The optimal antidepressant dosages remain controversial. This study aimed to analyze the efficacy of antidepressants and characterize their dose-response relationships in the treatments of major depressive disorders (MDD). METHODS: We searched multiple databases, including the Embase, Cochrane Central Register of Controlled Trials, PubMed, and Web of Science, for the studies that were conducted between January 8, 2016, and April 30, 2023. The studies are double-blinded, randomized controlled trials (RCTs) involving the adults (≥18 years) with MDD. The primary outcomes were efficacy of antidepressant and the dose-response relationships. A frequentist network meta-analysis was conducted, treating participants with various dosages of the same antidepressant as a single therapy. We also implemented the model-based meta-analysis (MBMA) using a Bayesian method to explore the dose-response relationships. RESULTS: The network meta-analysis comprised 135,180 participants from 602 studies. All the antidepressants were more effective than the placebo; toludesvenlafaxine had the highest odds ratio (OR) of 4.52 (95% confidence interval [CI]: 2.65-7.72), and reboxetine had the lowest OR of 1.34 (95%CI: 1.14-1.57). Moreover, amitriptyline, clomipramine, and reboxetine showed a linear increase in effect size from low to high doses. The effect size of toludesvenlafaxine increased significantly up to 80 mg/day and subsequently maintained the maximal dose up to 160 mg/day while the predictive curves of nefazodone were fairly flat in different dosages. CONCLUSIONS: Although most antidepressants were more efficacious than placebo in treating MDD, no consistent dose-response relationship between any antidepressants was observed. For most antidepressants, the maximum efficacy was achieved at lower or middle prescribed doses, rather than at the upper limit. REGISTRATION No. CRD42023427480; https://www.crd.york.ac.uk/prospero/display_record.php?
Humans ; Antidepressive Agents/therapeutic use* ; Depressive Disorder, Major/drug therapy* ; Dose-Response Relationship, Drug ; Randomized Controlled Trials as Topic